Estimation of domain discontinuities using Hierarchical Bayesian Fay-Herriot models
Section 2. The crime victimization survey

The Dutch crime victimization survey (CVS) is a long-standing survey conducted by Statistics Netherlands at an annual frequency with the purpose to publish reliable figures about crime rates, safety feelings, and satisfaction about police performance in the Netherlands. The CVS is designed to provide reliable figures at the national level and at the level of police districts, which is a subdivision of the Netherlands in 25 regions. The CVS is based on a stratified simple random sampling design for people aged 15 years or older residing in the Netherlands. Strata are formed by police regions to control the precision of these planned domain estimates. The sampling frame is based on the Dutch government’s register of all residents in the Netherlands, called Municipal Basis Administration. The yearly sample of the regular CVS is designed such that about 19,000 respondents are observed. The sample is equally divided over the strata, such that about 760 observations are obtained in each stratum. The general regression (GREG) estimator (Särndal, Swensson and Wretman, 1992) is used to estimate population parameters at the national level and for police districts.

The CVS has been redesigned in 2008. The data collection changed from a mixed-mode design via computer-assisted personal interviewing (CAPI) and computer-assisted telephone interviewing (CATI) to a sequential mixed-mode design that starts with web interviewing (WI) and is followed up for nonrespondents with CAPI and CATI. In addition the questionnaire is changed to improve the wording as well as the order of the questions. To quantify discontinuities induced by this redesign, the regular survey used for official publication purposes was conducted in parallel with the alternative survey approach with a sample size of about 6,000 respondents. In this application, the regular approach was based on the new survey design using WI, CATI and CAPI and the alternative approach was based on the old design using CAPI and CATI data collection only. The sample design for the parallel run is based on stratified simple random sampling where police districts are the strata, using proportional allocation. This results in a sample design that is optimal to estimate figures at the national level but suboptimal for domain estimation.

This survey reports on many different outcome variables. In the present study five key survey variables are considered, see Table 2.1. Estimates for these variables at the national level under the regular and alternative survey are specified in Table 2.2. The sample size allocated to the alternative approach is sufficiently large to estimate discontinuites at the national level using the GREG estimator but insufficient to estimate discontinuities at the domain level of the 25 police districts. The direct estimates for the discontinuities at the national level are indeed significantly different from zero, contrary to the unweighted averages of the direct domain estimates and their standard errors. To obtain more precise predictions for domain discontinuities a model-based small area estimation method based on area level models (Fay and Herriot, 1979) is proposed in the next section.


Table 2.1
Five key CVS survey variables considered in the present study
Table summary
This table displays the results of Five key CVS survey variables considered in the present study. The information is grouped by Variable (appearing as row headers), Description (appearing as column headers).
Variable Description
nuisance MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqk0Jf9crFfpeea0hj9ck=hEeeu0xXdf9arpi0xb9Lqpe 0dbvb9frpepeI8k8hiNsFfY=qqqrFfpie9qqpe0dd9q8qi0de9Fve9 Fve9pXqaaeaabiGaciaacaqabeaadaqaaqaaaOqaaGWaaiaa=5gaca WF1bGaa8xAaiaa=nhacaWFHbGaa8NBaiaa=ngacaWFLbaaaa@3F5A@ Perceived nuisance in the neighborhood on a ten point scale; this includes nuisance by drunk people, neigbours, or groups of youngsters, harassment, and drug related problems.
unsafe MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqk0Jf9crFfpeea0hj9ck=hEeeu0xXdf9arpi0xb9Lqpe 0dbvb9frpepeI8k8hiNsFfY=qqqrFfpie9qqpe0dd9q8qi0de9Fve9 Fve9pXqaaeaabiGaciaacaqabeaadaqaaqaaaOqaaGWaaiaa=vhaca WFUbGaa83Caiaa=fgacaWFMbGaa8xzaaaa@3D84@ Percentage of people feeling unsafe at times.
propvict MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqk0Jf9crFfpeea0hj9ck=hEeeu0xXdf9arpi0xb9Lqpe 0dbvb9frpepeI8k8hiNsFfY=qqqrFfpie9qqpe0dd9q8qi0de9Fve9 Fve9pXqaaeaabiGaciaacaqabeaadaqaaqaaaOqaaGWaaiaa=bhaca WFYbGaa83Baiaa=bhacaWF2bGaa8xAaiaa=ngacaWF0baaaa@3F7B@ Percentage of people saying to have been victim to property crime in the last 12 months.
offtot MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqk0Jf9crFfpeea0hj9ck=hEeeu0xXdf9arpi0xb9Lqpe 0dbvb9frpepeI8k8hiNsFfY=qqqrFfpie9qqpe0dd9q8qi0de9Fve9 Fve9pXqaaeaabiGaciaacaqabeaadaqaaqaaaOqaaGWaaiaa=9gaca WFMbGaa8Nzaiaa=rhacaWFVbGaa8hDaaaa@3D94@ Total number of offenses per 100 people.
satispol MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqk0Jf9crFfpeea0hj9ck=hEeeu0xXdf9arpi0xb9Lqpe 0dbvb9frpepeI8k8hiNsFfY=qqqrFfpie9qqpe0dd9q8qi0de9Fve9 Fve9pXqaaeaabiGaciaacaqabeaadaqaaqaaaOqaaGWaaiaa=nhaca WFHbGaa8hDaiaa=LgacaWFZbGaa8hCaiaa=9gacaWFSbaaaa@3F73@ Percentage of people satisfied with police at their last contact (if contact in last 12 months).

Table 2.2
GREG estimates for the regular and alternative survey approach averaged over districts and national level. Standard errors between brackets
Table summary
This table displays the results of GREG estimates for the regular and alternative survey approach averaged over districts and national level. Standard errors between brackets. The information is grouped by Variable (appearing as row headers), Average over 25 police districts and National level (appearing as column headers).
Variable Average over 25 police districts National level
regular alternative Δ ^ MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaebbnrfifHhDYfgasaacPqpw0le9 v8qqaqFK0lO8F4rqqrFfpu0de9GqFf0xc9qqpeuf0xe9q8qiYRWFGC k9vi=dbbf9v8Gq0db9qqpm0dXdHqpq0=vr0=vr0=edbeqabeWacmGa biqabeqabeqabeWabaGcbaqcgaOafuiLdqKbaKaaaaa@35C4@ regular alternative Δ ^ MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaebbnrfifHhDYfgasaacPqpw0le9 v8qqaqFK0lO8F4rqqrFfpu0de9GqFf0xc9qqpeuf0xe9q8qiYRWFGC k9vi=dbbf9v8Gq0db9qqpm0dXdHqpq0=vr0=vr0=edbeqabeWacmGa biqabeqabeqabeWabaGcbaqcgaOafuiLdqKbaKaaaaa@35C4@
offtot MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqk0Jf9crFfpeea0hj9ck=hEeeu0xXdf9arpi0xb9Lqpe 0dbvb9frpepeI8k8hiNsFfY=qqqrFfpie9qqpe0dd9q8qi0de9Fve9 Fve9pXqaaeaabiGaciaacaqabeaadaqaaqaaaOqaaGWaaiaa=9gaca WFMbGaa8Nzaiaa=rhacaWFVbGaa8hDaaaa@3D94@ 42.29 (4.73) 33.28 (5.73) 9.01 (7.69) 43.79 (1.07) 34.09 (1.04) 9.70 (1.49)
unsafe MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqk0Jf9crFfpeea0hj9ck=hEeeu0xXdf9arpi0xb9Lqpe 0dbvb9frpepeI8k8hiNsFfY=qqqrFfpie9qqpe0dd9q8qi0de9Fve9 Fve9pXqaaeaabiGaciaacaqabeaadaqaaqaaaOqaaGWaaiaa=vhaca WFUbGaa83Caiaa=fgacaWFMbGaa8xzaaaa@3D84@ 24.38 (2.03) 19.86 (2.87) 4.52 (3.57) 25.07 (0.44) 20.48 (0.52) 4.59 (0.68)
nuisance MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqk0Jf9crFfpeea0hj9ck=hEeeu0xXdf9arpi0xb9Lqpe 0dbvb9frpepeI8k8hiNsFfY=qqqrFfpie9qqpe0dd9q8qi0de9Fve9 Fve9pXqaaeaabiGaciaacaqabeaadaqaaqaaaOqaaGWaaiaa=5gaca WF1bGaa8xAaiaa=nhacaWFHbGaa8NBaiaa=ngacaWFLbaaaa@3F5A@ 1.61 (0.11) 1.28 (0.13) 0.33 (0.17) 1.67 (0.02) 1.34 (0.02) 0.33 (0.03)
satispol MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqk0Jf9crFfpeea0hj9ck=hEeeu0xXdf9arpi0xb9Lqpe 0dbvb9frpepeI8k8hiNsFfY=qqqrFfpie9qqpe0dd9q8qi0de9Fve9 Fve9pXqaaeaabiGaciaacaqabeaadaqaaqaaaOqaaGWaaiaa=nhaca WFHbGaa8hDaiaa=LgacaWFZbGaa8hCaiaa=9gacaWFSbaaaa@3F73@ 60.61 (4.23) 55.58 (6.88) 5.04 (8.21) 59.88 (0.92) 55.10 (1.25) 4.78 (1.55)
propvict MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqk0Jf9crFfpeea0hj9ck=hEeeu0xXdf9arpi0xb9Lqpe 0dbvb9frpepeI8k8hiNsFfY=qqqrFfpie9qqpe0dd9q8qi0de9Fve9 Fve9pXqaaeaabiGaciaacaqabeaadaqaaqaaaOqaaGWaaiaa=bhaca WFYbGaa83Baiaa=bhacaWF2bGaa8xAaiaa=ngacaWF0baaaa@3F7B@ 12.55 (1.60) 9.78 (2.19) 2.78 (2.77) 13.02 (0.36) 10.32 (0.39) 2.70 (0.53)

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